Machine learning algorithms are increasingly used in high-stakes domains.
For example, algorithms are used by judges to predict recidivism [1]}, by doctors to aid cancer screening [2]} and by banks to predict credit card fraud [3]}.
This is not without risk: a trained model can produce erroneous predictions, especially when it is asked to generalize beyond situations it is familiar with [4]}.
Often, algorithmic failure does not affect everyone equally: research has shown that in many AI systems, algorithmic failure disproportionately affects marginalized groups [5]}, [6]}.
